Digital Discovery of 100 diverse Quantum Experiments with PyTheus

Carlos Ruiz-Gonzalez1, Sören Arlt1, Jan Petermann1, Sharareh Sayyad1, Tareq Jaouni2, Ebrahim Karimi1,2, Nora Tischler3, Xuemei Gu1, and Mario Krenn1

1Max Planck Institute for the Science of Light, Erlangen, Germany.
2Nexus for Quantum Technologies, University of Ottawa, K1N 6N5, ON, Ottawa, Canada.
3Centre for Quantum Computation and Communication Technology (Australian Research Council), Centre for Quantum Dynamics, Griffith University, Brisbane, Australia.

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Abstract

Photons are the physical system of choice for performing experimental tests of the foundations of quantum mechanics. Furthermore, photonic quantum technology is a main player in the second quantum revolution, promising the development of better sensors, secure communications, and quantum-enhanced computation. These endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments was historically powered by human creativity but is recently being automated with advanced computer algorithms and artificial intelligence. While several computer-designed experiments have been experimentally realized, this approach has not yet been widely adopted by the broader photonic quantum optics community. The main roadblocks consist of most systems being closed-source, inefficient, or targeted to very specific use-cases that are difficult to generalize. Here, we overcome these problems with a highly-efficient, open-source digital discovery framework PyTheus, which can employ a wide range of experimental devices from modern quantum labs to solve various tasks. This includes the discovery of highly entangled quantum states, quantum measurement schemes, quantum communication protocols, multi-particle quantum gates, as well as the optimization of continuous and discrete properties of quantum experiments or quantum states. PyTheus produces interpretable designs for complex experimental problems which human researchers can often readily conceptualize. PyTheus is an example of a powerful framework that can lead to scientific discoveries – one of the core goals of artificial intelligence in science. We hope it will help accelerate the development of quantum optics and provide new ideas in quantum hardware and technology.

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[10] L. Sunil Chandran and Rishikesh Gajjala, "Graph-theoretic insights on the constructability of complex entangled states", arXiv:2304.06407, (2023).

[11] Tareq Jaouni, Xiaoqin Gao, Sören Arlt, Mario Krenn, and Ebrahim Karimi, "Experimental Solutions to the High-Dimensional Mean King's Problem", arXiv:2307.12938, (2023).

[12] Terry Rudolph, "Terry vs an AI, Round 1: Heralding single-rail (approximate?) 4-GHZ state from squeezed sources", arXiv:2303.05514, (2023).

[13] Jakob S. Kottmann and Francesco Scala, "Compact Effective Basis Generation: Insights from Interpretable Circuit Design", arXiv:2302.10660, (2023).

[14] Zeqiao Zhou, Yuxuan Du, Xu-Fei Yin, Shanshan Zhao, Xinmei Tian, and Dacheng Tao, "Optical Quantum Sensing for Agnostic Environments via Deep Learning", arXiv:2311.07203, (2023).

The above citations are from Crossref's cited-by service (last updated successfully 2024-02-27 16:35:57) and SAO/NASA ADS (last updated successfully 2024-02-27 16:35:58). The list may be incomplete as not all publishers provide suitable and complete citation data.